Vehicular traffic is endlessly increasing everywhere within the world and may cause terrible traffic jams at intersections. Traffic congestion and tidal flow are major facts that cause delays to emergency vehicles. Fire brigade officials, ambulances, and police officers often get delayed due to such congestion and traffic. With the use of the right technology, like Artificial Intelligence and real-time monitoring of the traffic, such a predicament can be moderated thereby saving the lives of the needy. The solution presented allows emergency Vehicle (EV) drivers to select the route of commute on the mobile application. Modules on that route selected are activated for the adaptive traffic control system. The driver’s GPS is dynamically updated on the cloud and fetched by the Raspberry Pi module. As the vehicle enters a given radius, the module checks for other EVs in the vicinity for priority assignment. The module starts traffic density detection and changes the traffic light states to clear the traffic if the density is above a certain threshold. After the EV comes closer to the signal, the light turns green irrespective of the traffic density. The camera cross validates if the EV has passed. Once it does, traffic signals switch to regular operation. This will allow the emergency vehicles to reach the destination on time and save the lives of those in need without delay. The entire solution is robust and reliable and can manage traffic efficiently keeping in mind the imperativeness of the emergency vehicles.


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    Titel :

    AI Powered Smart Traffic Control System for Emergency Vehicles


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Kumar, Amit (Herausgeber:in) / Senatore, Sabrina (Herausgeber:in) / Gunjan, Vinit Kumar (Herausgeber:in) / Kumar, Vedant (Autor:in) / Kumar, Siddhant (Autor:in) / Sreekar, L. (Autor:in) / Singh, Pradhuman (Autor:in) / Pai, Pratik (Autor:in) / Nimbre, Shivani (Autor:in) / Rathod, Surendra Singh (Autor:in)

    Erschienen in:

    ICDSMLA 2020 ; Kapitel : 59 ; 651-663


    Erscheinungsdatum :

    2021-11-09


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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